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Identifying the most vulnerable individuals requires effort to build a complete picture of the dynamic health effects of air pollution exposure, accounting for disparities across population subgroups.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We use generalized additive model to assess the likely changes in the hospitalisation and mortality rate as a result of exposure to PM2.5 and O\n                      <jats:sub>3<\/jats:sub>\n                      over the course of COVID-19 pandemic. We further disaggregate the population into detailed age categories and illustrate a shifting age profile of high-risk population groups. Additionally, we apply multivariable logistic regression to integrate demographic, socioeconomic and climatic characteristics with the pollution-related excess risk.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Overall, a total of 1,051,893 hospital admissions and 34,954 mortality for respiratory disease are recorded. The findings demonstrate a transition in the association between air pollutants and hospitalisation rates over time. For every 10\u00a0\u00b5g\/m\n                      <jats:sup>3<\/jats:sup>\n                      increase of PM2.5, the rate of hospital admission increased by 0.2% (95% CI: 0.1\u20130.7%) and 1.4% (1.0\u20131.7%) in the pre-pandemic and dynamic zero-COVID stage, respectively. Conversely, O\n                      <jats:sub>3<\/jats:sub>\n                      -related hospitalization rate would be increased by 0.7% (0.5\u20130.9%) in the pre-pandemic stage but lowered to 1.7% (1.5\u20131.9%) in the dynamic zero-COVID stage. Further assessment indicates a shift of high-risk people from children and young adolescents to the old, primarily the elevated hospitalization rates among the old people in Lianyungang (RR: 1.53, 95%CI: 1.46, 1.60) and Nantong (RR: 1.65, 95%CI: 1.57, 1.72) relative to those for children and young adolescents. Over the course of our study period, people with underlying diseases would have 26.5% (22.8\u201330.3%) and 12.7% (10.8\u201314.6%) higher odds of having longer hospitalisation and over 6 times higher odds of deaths after hospitalisation.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Our estimates provide the first comprehensive evidence on the dynamic pollution-health associations throughout the pandemic. The results suggest that age and underlying diseases collectively determines the disparities of pollution-related health effect across population subgroups, underscoring the urgency to identifying the most vulnerable individuals to air pollution.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12940-024-01083-1","type":"journal-article","created":{"date-parts":[[2024,4,12]],"date-time":"2024-04-12T23:42:37Z","timestamp":1712965357000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["The changing health effects of air pollution exposure for respiratory diseases: a multicity study during 2017\u20132022"],"prefix":"10.1186","volume":"23","author":[{"given":"Siyu","family":"Jiang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Longjuan","family":"Tang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhe","family":"Lou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haowei","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ling","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingqing","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruiyun","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhen","family":"Ding","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,4,13]]},"reference":[{"issue":"6","key":"1083_CR1","doi-asserted-by":"publisher","first-page":"e210","DOI":"10.1016\/S2542-5196(20)30107-8","volume":"4","author":"K Chen","year":"2020","unstructured":"Chen K, Wang M, Huang C, Kinney PL, Anastas PT. 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